Isometric Multi-Shape Matching

TL;DR

This paper introduces a new optimization approach for isometric multi-shape matching, ensuring cycle-consistency and outperforming existing methods in accuracy across multiple datasets.

Contribution

It proposes the first explicit formulation for isometric multi-shape matching and provides an efficient algorithm with proven convergence and complexity.

Findings

Achieves state-of-the-art accuracy on various datasets.

Ensures cycle-consistent multi-matchings by design.

Demonstrates superior performance over existing methods.

Abstract

Finding correspondences between shapes is a fundamental problem in computer vision and graphics, which is relevant for many applications, including 3D reconstruction, object tracking, and style transfer. The vast majority of correspondence methods aim to find a solution between pairs of shapes, even if multiple instances of the same class are available. While isometries are often studied in shape correspondence problems, they have not been considered explicitly in the multi-matching setting. This paper closes this gap by proposing a novel optimisation formulation for isometric multi-shape matching. We present a suitable optimisation algorithm for solving our formulation and provide a convergence and complexity analysis. Our algorithm obtains multi-matchings that are by construction provably cycle-consistent. We demonstrate the superior performance of our method on various datasets and set the new state-of-the-art in isometric multi-shape matching.